Due to the release of an official Google Analytics Data Export API, this module is now deprecated. We have an alternative python module based upon the real analytics API here, and an exploring tool with an automatic code generation capability here.
It is not official. It is not from Google. It is, however, very functional and very here. I present to you pyGAPI, the Juiced Google Analytics Python API. This module allows you to pull information from your incarnation of Google Analytics and employ it programatically into your reporting code.
Let us use iPython to peek through some code using pyGAPI.
[sourcecode language="python" light="true"] In [3]: from datetime import date In [4]: import pyGAPI In [5]: connector = pyGAPI.pyGAPI(username, password, website_id="1234567")
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Here we create a pyGAPI object. Behind the scenes, pyGAPI logs into Google Analytics, and downloads an identifier cookie. website_id is optional. If omitted, pyGAPI accesses the first website on the account’s list. To get a list of all the site IDs to which your site has access, run the function connector.list_sites().
[sourcecode language="python" light="true"] In [6]: connector.download_report('KeywordsReport', (date(2008,3,10), date(2008,3,31)), limit=5)
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Download a report into your pyGAPI object. KeywordsReport is the name of the report. It is followed by a tuple containing the start and end dates in python date format. limit is an optional parameter that specifies the number of entries that pyGAPI should pull down. By default, it will pull in all the entries up to a maximum of 10000. Lowering this number will certainly improve performance. The entries returned are ranked by Visits, so you should get the most significant values of the bunch.
[sourcecode language="python" light="true"] In [7]: print connector.csv() Keyword,Visits,Pages/Visit,Avg. Time on Site,% New Visits,Bounce Rate,Visits,Subscribe,Solutions,Goal Conversion Rate,Per Visit Goal Value juice analytics,356,5.935393258426966,314.061797752809,0.38764044642448425,0.29494380950927734,356,1.0,0.16292135417461395,1.1629213094711304,0.0 excel training,142,1.971830985915493,98.0774647887324,0.908450722694397,0.6901408433914185,142,1.0,0.0211267601698637,1.0211267471313477,0.0 excel charts,77,1.7922077922077921,95.0,0.9090909361839294,0.7792207598686218,77,1.0,0.03896103799343109,1.0389610528945923,0.0 excel skills,72,1.6527777777777777,75.29166666666667,0.9444444179534912,0.7083333134651184,72,1.0,0.0,1.0,0.0 colbert bump,70,1.3142857142857143,113.77142857142857,0.6428571343421936,0.8428571224212646,70,1.0,0.0,1.0,0.0
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This function displays your report in a nice excel-ready CSV format.
[sourcecode language="python" light="true"] In [8]: print connector.parse_csv_as_dicts(convert_numbers=True) [{'Avg. Time on Site': 314.06179775280901, 'Per Visit Goal Value': 0.0, 'Bounce Rate': 0.29494380950927734, 'Keyword': 'juice analytics', 'Visits': 356.0, 'Pages/Visit': 5.9353932584269664, 'Subscribe': 1.0, 'Solutions': 0.16292135417461395, '% New Visits': 0.38764044642448425, 'Goal Conversion Rate': 1.1629213094711304}, {'Avg. Time on Site': 98.077464788732399, 'Per Visit Goal Value': 0.0, 'Bounce Rate': 0.69014084339141846, 'Keyword': 'excel training', 'Visits': 142.0, 'Pages/Visit': 1.971830985915493, 'Subscribe': 1.0, 'Solutions': 0.021126760169863701, '% New Visits': 0.90845072269439697, 'Goal Conversion Rate': 1.0211267471313477}, {'Avg. Time on Site': 95.0, 'Per Visit Goal Value': 0.0, 'Bounce Rate': 0.77922075986862183, 'Keyword': 'excel charts', 'Visits': 77.0, 'Pages/Visit': 1.7922077922077921, 'Subscribe': 1.0, 'Solutions': 0.038961037993431091, '% New Visits': 0.90909093618392944, 'Goal Conversion Rate': 1.0389610528945923}, {'Avg. Time on Site': 75.291666666666671, 'Per Visit Goal Value': 0.0, 'Bounce Rate': 0.70833331346511841, 'Keyword': 'excel skills', 'Visits': 72.0, 'Pages/Visit': 1.6527777777777777, 'Subscribe': 1.0, 'Solutions': 0.0, '% New Visits': 0.94444441795349121, 'Goal Conversion Rate': 1.0}, {'Avg. Time on Site': 113.77142857142857, 'Per Visit Goal Value': 0.0, 'Bounce Rate': 0.84285712242126465, 'Keyword': 'colbert bump', 'Visits': 70.0, 'Pages/Visit': 1.3142857142857143, 'Subscribe': 1.0, 'Solutions': 0.0, '% New Visits': 0.6428571343421936, 'Goal Conversion Rate': 1.0}]
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This function goes the extra step and converts the CSV into a dictionary for easier programmatic use. By default, all entries will be returned as python strings. Setting convert_numbers to True, as we did here, will additionally parse the dictionary to turn all numbers into float values.
[sourcecode language="python" light="true"] In [9]: print connector.list_reports() ('ReferringSourcesReport', 'SearchEnginesReport', 'AllSourcesReport', 'KeywordsReport', 'CampaignsReport', 'AdVersionsReport', 'TopContentReport', 'ContentByTitleReport', 'ContentDrilldownReport', 'EntrancesReport', 'ExitsReport', 'GeoMapReport', 'LanguagesReport', 'HostnamesReport', 'SpeedsReport')
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This gets a list of all the reports that I have successfully tested thus far. All site-specific reports should work. A couple site-section specific reports should be included in the next update of pyGAPI.
Google is great and will release a real API soon, but until then you can download pyGAPI.